Analysis of Motorcycle Rider’s Posture Using sEMG Signals

Hand Gesture Recognition using sEMG Signals Based on Support Vector Machine

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) ◽

10.1109/itaic.2019.8785542 ◽

2019 ◽

Cited By ~ 3

Author(s):

Wenjun Chen ◽

Zhen Zhang

Keyword(s):

Support Vector Machine ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Support Vector ◽

Hand Gesture ◽

Semg Signals

Download Full-text

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

Sensors ◽

10.3390/s21103311 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3311

Author(s):

Riccardo Ballarini ◽

Marco Ghislieri ◽

Marco Knaflitz ◽

Valentina Agostini

Keyword(s):

Motor Task ◽

Optimal Number ◽

The Other ◽

Simulated Dataset ◽

Muscle Synergies ◽

Mean Square ◽

Real Dataset ◽

Traditional Approaches ◽

Selection Of ◽

Semg Signals

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

Download Full-text

The real-time recognition of upper limb micro motions based on sEMG signals

2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC) ◽

10.1109/icceic51584.2020.00009 ◽

2020 ◽

Author(s):

Changcheng Shi ◽

Sijia Ye ◽

Yehao Ma ◽

Guokun Zuo

Keyword(s):

Real Time ◽

Upper Limb ◽

The Real ◽

Semg Signals

Download Full-text

An Approach for Accurate Pattern Recognition of Four Hand Gestures Based on sEMG Signals

Proceedings of the 2019 2nd International Conference on Control and Robot Technology ◽

10.1145/3387304.3387323 ◽

2019 ◽

Author(s):

Mostafa Orban ◽

Xiaodong Zhang ◽

Zhufeng Lu ◽

Yi Zhang ◽

Hanzhe Li

Keyword(s):

Pattern Recognition ◽

Hand Gestures ◽

Semg Signals

Download Full-text

Integration of Forearm sEMG Signals with IMU Sensors for Trajectory Planning and Control of Assistive Robotic Arm

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8856699 ◽

2019 ◽

Cited By ~ 2

Author(s):

Bridget Schabron ◽

Adam Reust ◽

Jaydip Desai ◽

Yimesker Yihun

Keyword(s):

Trajectory Planning ◽

Robotic Arm ◽

Planning And Control ◽

And Control ◽

Trajectory Planning And Control ◽

Assistive Robotic ◽

Semg Signals

Download Full-text

Classification of sEMG signals of hand gestures based on energy features

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102948 ◽

2021 ◽

Vol 70 ◽

pp. 102948

Author(s):

Naveen Kumar Karnam ◽

Anish Chand Turlapaty ◽

Shiv Ram Dubey ◽

Balakrishna Gokaraju

Keyword(s):

Hand Gestures ◽

Semg Signals

Download Full-text

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/09544119211048011 ◽

2021 ◽

pp. 095441192110480

Author(s):

Lakshmi M Hari ◽

Gopinath Venugopal ◽

Swaminathan Ramakrishnan

Keyword(s):

Wavelet Transform ◽

Biceps Brachii ◽

Singular Value ◽

Time Frequency ◽

Singular Value Decomposition Method ◽

Dynamic Contractions ◽

Synchrosqueezed Wavelet Transform ◽

Value Decomposition ◽

Neuromuscular Conditions ◽

Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

Download Full-text